16-04-2021

Outline

  1. Compound identification
  2. Identification of spectra

O mnie

MichaƂ Burdukiewicz:

  • bioinformatyk (UMB, IBB PAN, BTU Cottbus-Senftenberg),
  • 12 lat doƛwiadczenia z R,
  • Stowarzyszenie WrocƂawskich UĆŒytkownikĂłw R (stwur.pl),
  • Fundacja Why R? (whyr.pl).

Mail: michalburdukiewicz@gmail.com

O mnie

O mnie

Compound identification

Compound identification in metabolomics

Sindelar, M., and Patti, G.J. (2020). Chemical Discovery in the Era of Metabolomics. J. Am. Chem. Soc. 142, 9097–9105.

Compound identification in metabolomics

Sindelar, M., and Patti, G.J. (2020). Chemical Discovery in the Era of Metabolomics. J. Am. Chem. Soc. 142, 9097–9105.

Fragmentation

Li, Y., Kuhn, M., Gavin, A.-C., and Bork, P. (2020). Identification of metabolites from tandem mass spectra with a machine learning approach utilizing structural features. Bioinformatics 36, 1213–1218.

Fragmentation

Mass spectrum of L-Glutamic acid (3TMS) measured on a Bruker impact II.

Challenges in compound identification

  • complex identification of structural isomers.

Complex identification of structural isomers

Mass spectrum of D-Glutamic acid (3TMS) measured on a Bruker impact II.

Challenges in compound identification

  • hard to identify structural isomers,
  • instrument-dependent fragmentation patterns.

Inter-instrument transferability

Oberacher, H., Pitterl, F., Siapi, E., Steele, B.R., Letzel, T., Grosse, S., Poschner, B., Tagliaro, F., Gottardo, R., Chacko, S.A., et al. (2012). On the inter-instrument and the inter-laboratory transferability of a tandem mass spectral reference library. 3. Focus on ion trap and upfront CID. Journal of Mass Spectrometry 47, 263–270.

Challenges in compound identification

  • hard to identify structural isomers,
  • instrument-dependent fragmentation patterns,
  • complex relationship between molecular structure and fragmentation spectra.

Connecting molecular structure and fragmentation spectra

Identification of spectra

Spectral databases

Databases: MassBank, MoNAi, METLIN and mzCloud.

Different search algorithms allow searching spectral databases with various levels of sensitivity and specificity.

Databases

Two compounds yielding the same set of ions at the same intensities due to similarity in structure and fragmentation pathways.

Stein, S. (2012). Mass Spectral Reference Libraries: An Ever-Expanding Resource for Chemical Identification. Anal. Chem. 84, 7274–7282.

Databases

Entirely different structures yielding ions of the same formulas,

Stein, S. (2012). Mass Spectral Reference Libraries: An Ever-Expanding Resource for Chemical Identification. Anal. Chem. 84, 7274–7282.

Databases

The same low mass ions from a substructure in common (benzoyl) for two very different precursor molecules.

Stein, S. (2012). Mass Spectral Reference Libraries: An Ever-Expanding Resource for Chemical Identification. Anal. Chem. 84, 7274–7282.

Databases

“Only 1.8% of spectra in an untargeted metabolomics experiment can be annotated.”

Silva, R.R. da, Dorrestein, P.C., and Quinn, R.A. (2015). Illuminating the dark matter in metabolomics. PNAS 112, 12549–12550.

Computational approaches for compound identification

Main approaches

Quantum chemistry

QCEIMS: Born-Oppenheimer molecular dynamics.

Weakness: computational cost.

Ásgeirsson, V., Bauer, C.A., and Grimme, S. (2017). Quantum chemical calculation of electron ionization mass spectra for general organic and inorganic molecules. Chem. Sci. 8, 4879–4895.

Combinatorial optimization methods

Enumerate all possible fragments of candidate structures by systematic bond cleavage.

E.g., MIDAS-G, MetFrag, MAGMa+.

Heuristic-based methods

Use simulated fragmentation libraries to define rules of fragmentation for highly similar compounds.

E.g., Mass Frontier, LipidBlast, LipidMatch.

Machine learning methods

Fragmentation can be seen as a stochastic homogeneous Markov process (e.g., CFM-ID., NEIMS.